R version 4.4.0 RC (2024-04-16 r86468 ucrt) -- "Puppy Cup"
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Platform: x86_64-w64-mingw32/x64

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> attach(asNamespace("epigrowthfit"))
> options(warn = 2L, error = if (interactive()) recover)
> 
> 
> ## disambiguate ########################################################
> 
> x <- c("c", "b", "a", "b", "a",
+        "a", "b", "c", "c", "c")
> y <- c("c[1]", "b[1]", "a[1]", "b[2]", "a[2]",
+        "a[3]", "b[3]", "c[2]", "c[3]", "c[4]")
> stopifnot(identical(disambiguate(x), y))
> x <- `names<-`(seq_along(x), x)
> y <- `names<-`(seq_along(x), y)
> stopifnot(identical(disambiguate(x, nms = TRUE), y))
> 
> 
> if (FALSE) {
+ ## rle1 ################################################################
+ 
+ x <- c(0, NA_real_, NaN, Inf, 1)
+ times <- 1:5
+ y <- rep.int(x, times)
+ rle.y <- rle1(y)
+ stopifnot(exprs = {
+ 	identical(rle.y, list(lengths = times, values = x))
+ 	identical(y, inverse.rle(rle.y))
+ })
+ 
+ 
+ ## locf ################################################################
+ 
+ x <- c(NA, NA, 1, NA, 2, 2, 3, NA)
+ stopifnot(exprs = {
+ 	identical(locf(x), c(NA, NA, 1, 1, 2, 2, 3, 3))
+ 	identical(locf(x, x0 = 0), c(0, 0, 1, 1, 2, 2, 3, 3))
+ })
+ }
> 
> 
> ## wald ################################################################
> 
> value <- rnorm(6L, 0, 1)
> se <- rlnorm(6L, 0, 0.1)
> level <- 0.95
> q <- qchisq(level, df = 1)
> W <- wald(value = value, se = se, level = level)
> stopifnot(exprs = {
+ 	is.double(W)
+ 	identical(dim(W), c(6L, 2L))
+ 	is.null(dimnames(W))
+ 	all.equal(W[, 1L], value - sqrt(q) * se)
+ 	all.equal(W[, 2L], value + sqrt(q) * se)
+ })
> 
> 
> ## cov2theta, theta2cov ################################################
> 
> set.seed(230719L)
> n <- 6L
> Sigma <- crossprod(matrix(rnorm(n * n), n, n))
> R <- chol(Sigma)
> R1 <- R * rep(1 / diag(R, names = FALSE), each = n)
> theta <- c(0.5 * log(diag(Sigma, names = FALSE)), R1[upper.tri(R1)])
> stopifnot(exprs = {
+ 	all.equal(cov2theta(Sigma), theta)
+ 	all.equal(theta2cov(theta), Sigma)
+ })
> 
> proc.time()
   user  system elapsed 
   0.76    0.12    0.89